Time Series Forecasting of Carbon Dioxide Concentration Based on Machine Learning Method

The rapid proliferation of industrial activities and urban development has markedly intensified anthropogenic climate change, underscoring the critical need for effective mitigation strategies targeting greenhouse gas emissions. While most existing carbon emission forecasts focus on quarterly or annual scales, accurate daily-level prediction is essential. This study utilizes daily atmospheric CO concentration data from January 25, 2015, to February 13, 2025, to forecast future CO levels. Two machine learning-based time series modelsProphet and Long Short-Term Memory (LSTM)are employed to perform daily-scale carbon emission forecasting. Descriptive statistical analysis reveals a clear upward trend and seasonal fluctuations in the data. Results indicate that while the Prophet model performs well on the training set, it exhibits limited generalization ability on the test set. In contrast, the LSTM model performs better on the test set, showing stronger adaptability to unseen data. This study provides insights into CO2 concentration trends, aiding climate change assessment, informing emission mitigation strategies, and guiding the selection of time series forecasting models.